THE INTEGRATION OF CHINA'S DUAL-TRACK SOCIAL SECURITY SYSTEM AND ITS IMPACT

Master'sMASTER OF SOCIAL SCIENCE

Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the efficacy of the proposed technique.Comment: Adversarial example attacks, Robust and Imperceptible, Human perceptual system, Neural Network

Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leverage the abundant unlabeled data. In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure. To accomplish this, we propose spatially adaptive networks (SPAD-Nets), a family of networks that dynamically adjust the structures to adapt to the spatial properties of input images, to build such a universal foundation model. We pre-train a spatial adaptive visual tokenizer (SPAD-VT) and then a spatial adaptive Vision Transformer (SPAD-ViT) via masked image modeling (MIM) on 55 public medical image datasets. The pre-training data comprises over 9 million image slices, representing the largest, most comprehensive, and most diverse dataset to our knowledge for pre-training universal foundation models for medical image analysis. The experimental results on downstream medical image classification and segmentation tasks demonstrate the superior performance and label efficiency of our model. Our code is available at https://github.com/function2-llx/PUMIT

Spin excitations in optimally P-doped BaFe2(As0.7P0.3)2superconductor

We use inelastic neutron scattering to study temperature and energy dependence of spin excitations in optimally P-doped BaFe2(As0.7P0.3)2 superconductor (Tc = 30 K) throughout the Brillouin zone. In the undoped state, spin waves and paramagnetic spin excitations of BaFe2As2 stem from antiferromagnetic (AF) ordering wave vector QAF= (1/-1,0) and peaks near zone boundary at (1/-1,1/-1) around 180 meV. Replacing 30% As by smaller P to induce superconductivity, low-energy spin excitations of BaFe2(As0.7P0.3)2form a resonance in the superconducting state and high-energy spin excitations now peaks around 220 meV near (1/-1,1/-1). These results are consistent with calculations from a combined density functional theory and dynamical mean field theory, and suggest that the decreased average pnictogen height in BaFe2(As0.7P0.3)2 reduces the strength of electron correlations and increases the effective bandwidth of magnetic excitations.Comment: 7 pages, 5 figures, with supplementar

Dual Active Bridge based Battery Charger for Plug-in Hybrid Electric Vehicle with Charging Current Containing Low Frequency Ripple

High power density is strongly preferable for the on-board battery charger of Plug-in Hybrid Electric Vehicle (PHEV). Wide band gap devices, such as Gallium Nitride HEMTs are being explored to push to higher switching frequency and reduce passive component size. In this case, the bulk DC link capacitor of AC-DC Power Factor Correction (PFC) stage, which is usually necessary to store ripple power of two times the line frequency in a DC current charging system, becomes a major barrier on power density. If low frequency ripple is allowed in the battery, the DC link capacitance can be significantly reduced. This paper focuses on the operation of a battery charging system, which is comprised of one Full Bridge (FB) AC-DC stage and one Dual Active Bridge (DAB) DC-DC stage, with charging current containing low frequency ripple at two times line frequency, designated as sinusoidal charging. DAB operation under sinusoidal charging is investigated. Two types of control schemes are proposed and implemented in an experimental prototype. It is proved that closed loop current control is the better. Full system test including both FB AC-DC stage and DAB DC-DC stage verified the concept of sinusoidal charging, which may lead to potentially very high power density battery charger for PHEV